Project Summary (Abstract) Obstructive sleep apnea (OSA) is a common sleep disorder affecting a significant portion of adult population in United States. OSA results in poor quality of sleep and is associated with increased cardiovascular risk, including hypertension. Blood pressure (BP) is known to increase with each OSA event, although the degree and frequency of BP increase will vary from individual to individual. Measuring BP during sleep studies could provide valuable information concerning cardiovascular risk in patients with OSA. However, BP is currently not measured during sleep evaluation because of the lack of a suitable method that is not intrusive to sleep. Pulse transit time (PTT) is a noninvasive measure that can be obtained from patient monitors in present use in polysomnography and has been suggested as a potential tool for BP estimation. However, PTT has rarely been studied for the purpose of evaluating BP during sleep. Analysis of PTT patterns has also been suggested as a tool to detect subtle arousals that accompany acute BP increase events. Therefore, PTT by itself may be an important measure of OSA's cardiovascular effect. Despite this potential, PTT has not been adopted as a routine sleep measure due to unresolved technical limitations, such as susceptibility to artifacts and the need for repeated calibrations, as well as lack of studies informing its clinical utility in predicting CV outcomes. In this proposal, we will test and validate an internally-developed novel artifact-robust PTT-based BP estimation algorithm using existing hospital-based data. The benefit of this dynamically-calibrated adaptive algorithm is that, unlike all other PTT-based BP estimation algorithms, it can accurately estimate BP over time without recalibration. Subsequently, we will apply this algorithm to construct BP patterns in existing sleep studies obtained as a part of a large observational study (MESA Sleep cohort). This cohort study has a wealth of information about participants' cardiovascular risks. We will test the utility of the characteristics of BP and PTT in predicting cardiovascular risks using the MESA data. This will be achieved by examining their associations with measures, such as heart mass and aortic stiffness, that are well- established markers of cardiovascular risk. We will also examine the association with the prevalence of cardiovascular disease. We anticipate that adding these novel measures (BP and PTT characteristics) to conventional measures of OSA will improve the identification of higher- risk groups of patients with OSA. Our findings will introduce a useful novel method that is compatible with routinely performed sleep studies and will facilitate better assessment of OSA.